On the use of Earth Observation to support estimates of national greenhouse gas emissions and sinks for the Global stocktake process: lessons learned from ESA-CCI RECCAP2.

The Global Stocktake (GST), implemented by the Paris Agreement, requires rapid developments in the capabilities to quantify annual greenhouse gas (GHG) emissions and removals consistently from the global to the national scale and improvements to national GHG inventories. In particular, new capabilities are needed for accurate attribution of sources and sinks and their trends to natural and anthropogenic processes. On the one hand, this is still a major challenge as national GHG inventories follow globally harmonized methodologies based on the guidelines established by the Intergovernmental Panel on Climate Change, but these can be implemented differently for individual countries. Moreover, in many countries the capability to systematically produce detailed and annually updated GHG inventories is still lacking. On the other hand, spatially-explicit datasets quantifying sources and sinks of carbon dioxide, methane and nitrous oxide emissions from Earth Observations (EO) are still limited by many sources of uncertainty. While national GHG inventories follow diverse methodologies depending on the availability of activity data in the different countries, the proposed comparison with EO-based estimates can help improve our understanding of the comparability of the estimates published by the different countries. Indeed, EO networks and satellite platforms have seen a massive expansion in the past decade, now covering a wide range of essential climate variables and offering high potential to improve the quantification of global and regional GHG budgets and advance process understanding. Yet, there is no EO data that quantifies greenhouse gas fluxes directly, rather there are observations of variables or proxies that can be transformed into fluxes using models. Here, we report results and lessons from the ESA-CCI RECCAP2 project, whose goal was to engage with National Inventory Agencies to improve understanding about the methods used by each community to estimate sources and sinks of GHGs and to evaluate the potential for satellite and in-situ EO to improve national GHG estimates. Based on this dialogue and recent studies, we discuss the potential of EO approaches to provide estimates of GHG budgets that can be compared with those of national GHG inventories. We outline a roadmap for implementation of an EO carbon-monitoring program that can contribute to the Paris Agreement.

Tracking 21st century anthropogenic and natural carbon fluxes through model-data integration.

Monitoring the implementation of emission commitments under the Paris agreement relies on accurate estimates of terrestrial carbon fluxes. Here, we assimilate a 21st century observation-based time series of woody vegetation carbon densities into a bookkeeping model (BKM). This approach allows us to disentangle the observation-based carbon fluxes by terrestrial woody vegetation into anthropogenic and environmental contributions. Estimated emissions (from land-use and land cover changes) between 2000 and 2019 amount to 1.4 PgC yr-1, reducing the difference to other carbon cycle model estimates by up to 88% compared to previous estimates with the BKM (without the data assimilation). Our estimates suggest that the global woody vegetation carbon sink due to environmental processes (1.5 PgC yr-1) is weaker and more susceptible to interannual variations and extreme events than estimated by state-of-the-art process-based carbon cycle models. These findings highlight the need to advance model-data integration to improve estimates of the terrestrial carbon cycle under the Global Stocktake.

Catchment soils as a factor of trace metal accumulation in sediments of the reservoir Klingenberg (eastern Ore Mountains, Germany).

The release and accumulation dynamics of trace metals in soils and aquatic sediments were exemplarily investigated in the catchment area of the Reservoir Klingenberg (Germany). Catchment soils were examined for mobilizable and total concentrations of arsenic (As), cadmium (Cd), chrome (Cr), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), and zinc (Zn) and compared with trace metal quantities accumulated in riverbed and reservoir sediments. The comparison of all samples showed relatively small variations of Cr (7.96-46.0 mg/kg), Fe (7.79-40.4 g/kg), and Ni (6.06-56.5 mg/kg), while stronger differences were found for As (11.2-164 mg/kg), Cd (0.14-30.5 mg/kg), Mn (0.08-1.84 g/kg), Pb (20.7-183 mg/kg), and Zn (69.1-916 mg/kg). The catchment soils were slightly enriched by Cd, Pb, and Zn. Especially Cd and Zn were characterized by large mobilizable proportions. The mean trace metal concentrations in riverbed sediments were higher than in catchment soils, while reservoir sediments accumulated the highest amounts of the analyzed elements. The enrichment of trace metals in reservoir sediments was generally determined by the sedimentation of fine particles, while the distribution of As, Fe, and Mn was additionally impacted by redox conditions. For Cd and Zn, which in comparison were most enriched in riverbed and reservoir sediments, a significant release from soils by leaching processes was observed. The accumulation of As and Pb in reservoir sediments was influenced to a greater extent by soil erosion and by anthropogenic or chalcogen sources in the catchment.

Sectoral performance analysis of national greenhouse gas emission inventories by means of neural networks.

Annual greenhouse gas emissions have increased more than threefold between 1950 and 2014, posing a major threat to the integrity of the entire earth system and subsequently to humankind. Consequently, roadmaps towards low-carbon pathways are urgently needed. Our study contributes to a more detailed understanding of the dynamics of country based emission patterns and uses them to discuss prospective low-carbon pathways for countries. As availability of databases on sectoral emissions substantially increased, we employ machine learning techniques to classify emission features and pathways. By doing so, 18 representative emission patterns are derived. Overall emissions from seven sectors and for 167 countries covering the time span from 1950 to 2014 have been used in the analyses. The following significant trends can be observed: a) increasing per capita emissions due to growing fossil fuel use in many parts of the world, b) a decline in per capita emissions in some countries, and c) a shift in the emission shares, i.e., a reduction of agricultural and land use contributions in certain regions. Using the emission patterns, their dynamics, and best performing countries as role models, we show the possibility for gaining a decent human development without significantly increasing per capita emissions.